Summarize News Articles with NLP and TensorFlow

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prerequisites
intermediate Python • beginner TensorFlow/Keras • basics of NLP • basics of deep learning
skills learned
convert an abstractive text summarization dataset to an extractive one • train a deep learning model to perform extractive text summarization
Souradip Chakraborty and Sayak Paul
5 weeks · 7-10 hours per week · INTERMEDIATE
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Look inside
News Media Corp needs to be quick if they want to get ahead of their competitors. Their current news frontpage is put together manually, in a time consuming process where human editors create flashcards that summarize articles. It’s too slow—so senior management wants to supercharge the process using natural language processing. To get this built, they’ve turned to you. Your challenge in this liveProject is to create an NLP model that can reduce turnaround time for news editors with an automatic text summarizer. To do this, you’ll need to prepare and process your dataset with tokenization and padding, extract meaningful statistics from it, and finally use your dataset to train a deep learning model that can speedily summarize a body text.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

project authors

Souradip Chakraborty
Souradip Chakraborty is a Data Scientist at Walmart Labs, India in the field of Statistical Machine Learning. A Google Developers Expert in Machine Learning, he has published several US Patents and papers and has been a regular speaker at various workshops and conferences.
Sayak Paul
Sayak is employed at Carted where he works on representation learning from product webpages, as well as other NLP use cases. His interests are in the general representation learning area and include self-supervision, semi-supervision, multimodal alignment, and model robustness

prerequisites

The liveProject is for intermediate Python programmers who know the basics of deep learning and NLP. To begin this liveProject, you should be familiar with the following topics:

TOOLS
  • Jupyter Notebooks
  • pandas
  • scikit-learn
  • Keras
TECHNIQUES
  • Basic data manipulation and visualization
  • Rouge scoring
  • Tokenization
  • Word embeddings
  • Neural network architectures like convolutional neural networks and recurrent neural networks

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